{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fc2d3eaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:00.643255Z",
     "iopub.status.busy": "2023-05-27T03:05:00.642827Z",
     "iopub.status.idle": "2023-05-27T03:05:10.105613Z",
     "shell.execute_reply": "2023-05-27T03:05:10.104625Z"
    },
    "id": "wHsFZo6akD8M",
    "outputId": "ad2dfb82-1745-4b95-cb4e-6e839c1f5d72",
    "papermill": {
     "duration": 9.47148,
     "end_time": "2023-05-27T03:05:10.108120",
     "exception": false,
     "start_time": "2023-05-27T03:05:00.636640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
    "batch_size = 8\n",
    "\n",
    "device=\"cuda\"\n",
    "\n",
    "#uncomment when using colab or local cpu\n",
    "# train_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=True, transform=transform, download=True)\n",
    "# test_dataset = torchvision.datasets.cifar.CIFAR100(root='cifar100', train=False, transform=transform, download=True)\n",
    "# train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "# test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)\n",
    "\n",
    "#uncomment when using kaggle\n",
    "\n",
    "train_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\n",
    "\n",
    "test_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "290d6bae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:10.118326Z",
     "iopub.status.busy": "2023-05-27T03:05:10.117265Z",
     "iopub.status.idle": "2023-05-27T03:05:10.587762Z",
     "shell.execute_reply": "2023-05-27T03:05:10.586294Z"
    },
    "id": "VjkCK8NykPMb",
    "outputId": "a059791d-0629-4634-9dfa-5615889ddfa3",
    "papermill": {
     "duration": 0.477923,
     "end_time": "2023-05-27T03:05:10.590374",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.112451",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# functions to show an image\n",
    "\n",
    "\n",
    "def imshow(img):\n",
    "    img = img / 2 + 0.5     # unnormalize\n",
    "    npimg = img.numpy()\n",
    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# get some random training images\n",
    "dataiter = iter(train_loader)\n",
    "# images, labels = dataiter.next()\n",
    "images, labels = next(dataiter)\n",
    "\n",
    "# show images\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "# print labels\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4398380f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:10.602195Z",
     "iopub.status.busy": "2023-05-27T03:05:10.601885Z",
     "iopub.status.idle": "2023-05-27T03:05:10.606997Z",
     "shell.execute_reply": "2023-05-27T03:05:10.605976Z"
    },
    "papermill": {
     "duration": 0.013591,
     "end_time": "2023-05-27T03:05:10.609247",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.595656",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee4df0b8",
   "metadata": {
    "papermill": {
     "duration": 0.004619,
     "end_time": "2023-05-27T03:05:10.618636",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.614017",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## CA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ecd6dff0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:10.629769Z",
     "iopub.status.busy": "2023-05-27T03:05:10.629096Z",
     "iopub.status.idle": "2023-05-27T03:05:10.638862Z",
     "shell.execute_reply": "2023-05-27T03:05:10.637868Z"
    },
    "papermill": {
     "duration": 0.017577,
     "end_time": "2023-05-27T03:05:10.640949",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.623372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ChannelAttention(nn.Module):\n",
    "    def __init__(self, in_channels, reduction_ratio=16):\n",
    "        super(ChannelAttention, self).__init__()\n",
    "        \n",
    "        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(in_channels, in_channels // reduction_ratio),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(in_channels // reduction_ratio, in_channels),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # 输入x的尺寸：[batch_size, in_channels, height, width]\n",
    "        \n",
    "        # 全局平均池化，输出尺寸：[batch_size, in_channels, 1, 1]\n",
    "        feat = self.avg_pool(x)\n",
    "        \n",
    "        # 压缩通道维度，输出尺寸：[batch_size, in_channels // reduction_ratio, 1, 1]\n",
    "        feat = feat.view(feat.size(0), -1)\n",
    "        \n",
    "        # 全连接层，输出尺寸：[batch_size, in_channels]\n",
    "        feat = self.fc(feat)\n",
    "        \n",
    "        # 将通道权重应用到输入特征图上，输出尺寸：[batch_size, in_channels, height, width]\n",
    "        feat = torch.unsqueeze(feat, 2)\n",
    "        feat = torch.unsqueeze(feat, 3)\n",
    "        feat = x * feat\n",
    "        \n",
    "        return feat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b99c9cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:10.652127Z",
     "iopub.status.busy": "2023-05-27T03:05:10.651853Z",
     "iopub.status.idle": "2023-05-27T03:05:10.686060Z",
     "shell.execute_reply": "2023-05-27T03:05:10.685125Z"
    },
    "id": "1mY2iwmCkcpA",
    "papermill": {
     "duration": 0.042271,
     "end_time": "2023-05-27T03:05:10.688204",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.645933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def conv3x3(in_planes, out_planes, stride=1):\n",
    "    \"3x3 convolution with padding\"\n",
    "    return nn.Conv2d(\n",
    "        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "    )\n",
    "\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = conv3x3(inplanes, planes, stride)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.conv2 = conv3x3(planes, planes)\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(\n",
    "        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n",
    "    ):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(planes)\n",
    "        self.conv2 = nn.Conv2d(\n",
    "            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n",
    "        )\n",
    "        self.bn2 = nn.BatchNorm2d(planes)\n",
    "        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * 4)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        self.downsample = downsample\n",
    "        self.stride = stride\n",
    "\n",
    "        if use_triplet_attention:\n",
    "            self.triplet_attention = TripletAttention(planes * 4, 16)\n",
    "        else:\n",
    "            self.triplet_attention = None\n",
    "\n",
    "    def forward(self, x):\n",
    "        residual = x\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv2(out)\n",
    "        out = self.bn2(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            residual = self.downsample(x)\n",
    "\n",
    "        if not self.triplet_attention is None:\n",
    "            out = self.triplet_attention(out)\n",
    "\n",
    "        out += residual\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n",
    "class ResNet(nn.Module):\n",
    "    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n",
    "        self.inplanes = 64\n",
    "        super(ResNet, self).__init__()\n",
    "        self.network_type = network_type\n",
    "        # different model config between ImageNet and CIFAR\n",
    "        if network_type == \"ImageNet\":\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n",
    "            )\n",
    "            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "            self.avgpool = nn.AvgPool2d(7)\n",
    "        else:\n",
    "            self.conv1 = nn.Conv2d(\n",
    "                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n",
    "            )\n",
    "\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n",
    "        self.ca_1 = ChannelAttention(64)\n",
    "        self.layer2 = self._make_layer(\n",
    "            block, 128, layers[1], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.ca_1 = ChannelAttention(128)\n",
    "        self.layer3 = self._make_layer(\n",
    "            block, 256, layers[2], stride=2, att_type=att_type\n",
    "        )\n",
    "        self.ca_1 = ChannelAttention(256)\n",
    "        self.layer4 = self._make_layer(\n",
    "            block, 512, layers[3], stride=2, att_type=att_type\n",
    "        )\n",
    "\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        init.kaiming_normal_(self.fc.weight)\n",
    "        for key in self.state_dict():\n",
    "            if key.split(\".\")[-1] == \"weight\":\n",
    "                if \"conv\" in key:\n",
    "                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n",
    "                if \"bn\" in key:\n",
    "                    if \"SpatialGate\" in key:\n",
    "                        self.state_dict()[key][...] = 0\n",
    "                    else:\n",
    "                        self.state_dict()[key][...] = 1\n",
    "            elif key.split(\".\")[-1] == \"bias\":\n",
    "                self.state_dict()[key][...] = 0\n",
    "\n",
    "    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(\n",
    "                    self.inplanes,\n",
    "                    planes * block.expansion,\n",
    "                    kernel_size=1,\n",
    "                    stride=stride,\n",
    "                    bias=False,\n",
    "                ),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(\n",
    "            block(\n",
    "                self.inplanes,\n",
    "                planes,\n",
    "                stride,\n",
    "                downsample,\n",
    "                use_triplet_attention=att_type == \"TripletAttention\",\n",
    "            )\n",
    "        )\n",
    "        self.inplanes = planes * block.expansion\n",
    "        for i in range(1, blocks):\n",
    "            layers.append(\n",
    "                block(\n",
    "                    self.inplanes,\n",
    "                    planes,\n",
    "                    use_triplet_attention=att_type == \"TripletAttention\",\n",
    "                )\n",
    "            )\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        if self.network_type == \"ImageNet\":\n",
    "            x = self.avgpool(x)\n",
    "        else:\n",
    "            x = F.avg_pool2d(x, 4)\n",
    "        x = x.view(x.size(0), -1)\n",
    "        x = self.fc(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "def ResidualNet(network_type, depth, num_classes, att_type):\n",
    "\n",
    "    assert network_type in [\n",
    "        \"ImageNet\",\n",
    "        \"CIFAR10\",\n",
    "        \"CIFAR100\",\n",
    "    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n",
    "    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n",
    "\n",
    "    if depth == 18:\n",
    "        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n",
    "\n",
    "    elif depth == 34:\n",
    "        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
    "\n",
    "    elif depth == 50:\n",
    "        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n",
    "\n",
    "    elif depth == 101:\n",
    "        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "216e8685",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:10.699217Z",
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     "iopub.status.idle": "2023-05-27T03:05:13.902171Z",
     "shell.execute_reply": "2023-05-27T03:05:13.901220Z"
    },
    "id": "otA9CIgdnXfW",
    "outputId": "cc48fb07-5d0f-4446-9244-8b2052ef8020",
    "papermill": {
     "duration": 3.212936,
     "end_time": "2023-05-27T03:05:13.906136",
     "exception": false,
     "start_time": "2023-05-27T03:05:10.693200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (layer1): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (ca_1): ChannelAttention(\n",
      "    (avg_pool): AdaptiveAvgPool2d(output_size=1)\n",
      "    (fc): Sequential(\n",
      "      (0): Linear(in_features=256, out_features=16, bias=True)\n",
      "      (1): ReLU(inplace=True)\n",
      "      (2): Linear(in_features=16, out_features=256, bias=True)\n",
      "      (3): Sigmoid()\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): BasicBlock(\n",
      "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): BasicBlock(\n",
      "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    )\n",
      "  )\n",
      "  (fc): Linear(in_features=512, out_features=100, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "device=\"cuda\"\n",
    "net=ResidualNet(\"CIFAR100\",18,100,None).to(device)\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee0fae74",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-05-27T03:05:13.923246Z"
    },
    "id": "35VI01P_kqal",
    "outputId": "e9a6a4ce-3d52-4e8d-b58a-f7727f7c745b",
    "papermill": {
     "duration": 0.014558,
     "end_time": "2023-05-27T03:05:13.926241",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.0005)\n",
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d76017de",
   "metadata": {
    "id": "ck0do7nrmNTQ",
    "papermill": {
     "duration": 0.005078,
     "end_time": "2023-05-27T03:05:13.936588",
     "exception": false,
     "start_time": "2023-05-27T03:05:13.931510",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07b53d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:05:13.948760Z",
     "iopub.status.busy": "2023-05-27T03:05:13.948138Z",
     "iopub.status.idle": "2023-05-27T03:05:13.959787Z",
     "shell.execute_reply": "2023-05-27T03:05:13.958997Z"
    },
    "id": "xKlx2mCxlE02",
    "papermill": {
     "duration": 0.019826,
     "end_time": "2023-05-27T03:05:13.961643",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def train(epoch):\n",
    "    net.train()\n",
    "    # Loop over each batch from the training set\n",
    "    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n",
    "    for batch_idx, (data, target) in enumerate(train_tqdm):\n",
    "        # Copy data to GPU if needed\n",
    "        data = data.to(device)\n",
    "        target = target.to(device)\n",
    "        # Zero gradient buffers\n",
    "        optimizer.zero_grad()\n",
    "        # Pass data through the network\n",
    "        output = net(data)\n",
    "        # Calculate loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backpropagate\n",
    "        loss.backward()\n",
    "        # Update weights\n",
    "        optimizer.step()  # w - alpha * dL / dw\n",
    "        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n",
    "\n",
    "def validate(lossv,top1AccuracyList,top5AccuracyList):\n",
    "    net.eval()\n",
    "    val_loss = 0\n",
    "    top1Correct = 0\n",
    "    top5Correct = 0\n",
    "    for index,(data, target) in enumerate(test_loader):\n",
    "        data = data.to(device)\n",
    "        labels = target.to(device)\n",
    "        outputs = net(data)\n",
    "        val_loss += criterion(outputs, labels).data.item()\n",
    "        _, top1Predicted = torch.max(outputs.data, 1)\n",
    "        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n",
    "        top1Correct += (top1Predicted == labels).cpu().sum().item()\n",
    "        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n",
    "        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n",
    "\n",
    "    val_loss /= len(test_loader)\n",
    "    lossv.append(val_loss)\n",
    "\n",
    "    top1Acc=100*top1Correct / len(test_loader.dataset)\n",
    "    top5Acc=100*top5Correct / len(test_loader.dataset)\n",
    "    top1AccuracyList.append(top1Acc)\n",
    "    top5AccuracyList.append(top5Acc)\n",
    "    \n",
    "    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n",
    "#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n",
    "#     accv.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43ac2683",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2023-05-27T03:43:38.635954Z",
     "shell.execute_reply": "2023-05-27T03:43:38.633373Z"
    },
    "id": "pTWz8B3Dl9gu",
    "outputId": "7aed78c3-6ee9-4b39-d339-02b00a81db23",
    "papermill": {
     "duration": 2304.671308,
     "end_time": "2023-05-27T03:43:38.638200",
     "exception": false,
     "start_time": "2023-05-27T03:05:13.966892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|██████████| 5000/5000 [01:50<00:00, 45.10it/s, loss=2.09]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8632, Top1Accuracy: 2801/10000 (28%) Top5Accuracy: 5798.0/10000 (58%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|██████████| 5000/5000 [01:41<00:00, 49.41it/s, loss=2.63]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.3247, Top1Accuracy: 3965/10000 (40%) Top5Accuracy: 7148.0/10000 (71%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|██████████| 5000/5000 [01:42<00:00, 48.97it/s, loss=1.95]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 1.9417, Top1Accuracy: 4754/10000 (48%) Top5Accuracy: 7838.0/10000 (78%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|██████████| 5000/5000 [01:42<00:00, 48.69it/s, loss=1.37]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 1.8647, Top1Accuracy: 5047/10000 (50%) Top5Accuracy: 8009.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|██████████| 5000/5000 [01:42<00:00, 48.60it/s, loss=1.18]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 1.7587, Top1Accuracy: 5396/10000 (54%) Top5Accuracy: 8247.0/10000 (82%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|██████████| 5000/5000 [01:42<00:00, 48.78it/s, loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 1.8298, Top1Accuracy: 5402/10000 (54%) Top5Accuracy: 8186.0/10000 (82%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|██████████| 5000/5000 [01:43<00:00, 48.30it/s, loss=0.887]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 1.9546, Top1Accuracy: 5488/10000 (55%) Top5Accuracy: 8251.0/10000 (83%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|██████████| 5000/5000 [01:44<00:00, 48.05it/s, loss=0.421]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.1176, Top1Accuracy: 5429/10000 (54%) Top5Accuracy: 8156.0/10000 (82%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|██████████| 5000/5000 [01:44<00:00, 47.89it/s, loss=0.579]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.2637, Top1Accuracy: 5384/10000 (54%) Top5Accuracy: 8163.0/10000 (82%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|██████████| 5000/5000 [01:44<00:00, 47.70it/s, loss=0.33]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.4472, Top1Accuracy: 5280/10000 (53%) Top5Accuracy: 7975.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|██████████| 5000/5000 [01:44<00:00, 47.91it/s, loss=0.258]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.5241, Top1Accuracy: 5307/10000 (53%) Top5Accuracy: 8049.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|██████████| 5000/5000 [01:43<00:00, 48.22it/s, loss=0.405]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6097, Top1Accuracy: 5283/10000 (53%) Top5Accuracy: 8003.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|██████████| 5000/5000 [01:42<00:00, 48.89it/s, loss=0.592]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.6170, Top1Accuracy: 5348/10000 (53%) Top5Accuracy: 7985.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|██████████| 5000/5000 [01:45<00:00, 47.31it/s, loss=0.431]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.7676, Top1Accuracy: 5358/10000 (54%) Top5Accuracy: 7981.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|██████████| 5000/5000 [01:44<00:00, 48.04it/s, loss=0.0178]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8123, Top1Accuracy: 5264/10000 (53%) Top5Accuracy: 7941.0/10000 (79%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|██████████| 5000/5000 [01:43<00:00, 48.50it/s, loss=0.357]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8550, Top1Accuracy: 5296/10000 (53%) Top5Accuracy: 7972.0/10000 (80%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|██████████| 5000/5000 [01:45<00:00, 47.41it/s, loss=0.0602]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.9593, Top1Accuracy: 5265/10000 (53%) Top5Accuracy: 7941.0/10000 (79%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|██████████| 5000/5000 [01:42<00:00, 48.75it/s, loss=0.0785]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.8882, Top1Accuracy: 5319/10000 (53%) Top5Accuracy: 7931.0/10000 (79%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|██████████| 5000/5000 [01:47<00:00, 46.65it/s, loss=0.00732]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.9384, Top1Accuracy: 5276/10000 (53%) Top5Accuracy: 7942.0/10000 (79%)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|██████████| 5000/5000 [01:43<00:00, 48.19it/s, loss=0.00444]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Validation set: Average loss: 2.9826, Top1Accuracy: 5345/10000 (53%) Top5Accuracy: 7901.0/10000 (79%)\n",
      "\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "lossv = []\n",
    "top1AccuracyList = []   # top1准确率列表\n",
    "top5AccuracyList = []   # top5准确率列表\n",
    "max_epoch = 20\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    train(epoch)\n",
    "    with torch.no_grad():\n",
    "        validate(lossv,top1AccuracyList,top5AccuracyList)\n",
    "        \n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e0ee302",
   "metadata": {
    "id": "5qEPmaZO1NU3",
    "papermill": {
     "duration": 9.189623,
     "end_time": "2023-05-27T03:43:56.902858",
     "exception": false,
     "start_time": "2023-05-27T03:43:47.713235",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3fa8796",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-05-27T03:44:15.316814Z",
     "iopub.status.busy": "2023-05-27T03:44:15.315756Z",
     "iopub.status.idle": "2023-05-27T03:44:16.110353Z",
     "shell.execute_reply": "2023-05-27T03:44:16.109465Z"
    },
    "id": "Z1-D3LkhvzDq",
    "outputId": "e29901ea-e0b2-4141-f18e-a6431171ec0f",
    "papermill": {
     "duration": 9.750645,
     "end_time": "2023-05-27T03:44:16.112412",
     "exception": false,
     "start_time": "2023-05-27T03:44:06.361767",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'validation top5 accuracy')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# 绘制曲线\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), lossv)\n",
    "plt.title('validation loss')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top1AccuracyList)\n",
    "plt.title('validation top1 accuracy')\n",
    "\n",
    "plt.figure(figsize=(5,3))\n",
    "plt.plot(np.arange(1, max_epoch + 1), top5AccuracyList)\n",
    "plt.title('validation top5 accuracy')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ebe074c",
   "metadata": {
    "id": "fyWGvlbHup5w",
    "papermill": {
     "duration": 9.432639,
     "end_time": "2023-05-27T03:44:34.636142",
     "exception": false,
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